Autonomous Orbit Determination and Station-Keeping Workflow

This workflow automates the most repetitive and latency-sensitive task in satellite operations: determining orbital position and planning corrective maneuvers. By fusing onboard sensor data from star trackers, GPS receivers, and inter-satellite ranging, an orchestration agent continuously calculates the spacecraft's state vector. This eliminates the daily cycle of downloading telemetry to ground stations for human analysis, reducing operational overhead and enabling real-time response to orbital perturbations. The business value is direct: reduced ground segment labor, lower risk of collision from delayed maneuvers, and optimized fuel usage extending asset life.

Implementation integrates with existing flight software via a containerized agent architecture, using frameworks like NASA's F Prime or ROS 2 for reliable messaging. The orchestrator employs an extended Kalman filter for state estimation and a fuel-optimal Lambert solver for maneuver planning. Critical controls include a configurable delta-V threshold requiring ground approval before execution and comprehensive telemetry logging to a system of record like COSMOS. Rollout is phased, starting with low-risk demonstration orbits, with digital twin simulation in GMAT or STK validating every logic path before flight.

This workflow automates the critical, repetitive bottleneck of ground-based orbit determination and maneuver planning. For satellite operators, the savings are direct: a 60-80% reduction in daily tracking operations labor and a 5-15% fuel saving from more efficient, continuous low-thrust adjustments. The architecture fuses GPS, star tracker, and ranging data through a Kalman filter agent, feeding a station-keeping planner that models perturbation forces and calculates optimal thruster firings. Implementation requires integrating these agents with the flight software bus, propulsion controller, and a secure command uplink for overrides.

Implementation centers on a LangGraph or custom orchestrator managing agent handoffs, with strict controls. The Kalman filter agent must validate sensor health; the planner agent operates within pre-defined fuel and thruster duty-cycle limits. All autonomous decisions are logged with full rationale for ground audit. Rollout is phased: first in a high-fidelity digital twin (STK, GMAT) for validation, then shadow mode on-orbit, before full autonomy. This creates a resilient, fuel-aware autonomy layer that protects asset longevity and operational margins.

This workflow automates the critical, repetitive cycle of position estimation and orbital correction, a major operational bottleneck for satellite fleets. The business value is direct: it reduces ground station tracking costs by 70-90% and minimizes fuel expenditure through optimal maneuver planning, directly extending asset life and improving EBITDA for commercial operators. The architecture fuses star tracker, GPS, and ranging data via a Kalman filter agent, feeding a maneuver optimization agent that calculates and validates thruster commands against propulsion constraints before execution.

Implementation follows a phased delivery model, starting with a high-fidelity digital twin for validation against historical mission data. The first flight increment deploys the orbit determination agent with a human-in-the-loop approval gate for maneuvers. Subsequent phases incrementally enable autonomous optimization and closed-loop execution, with rigorous monitoring of fuel delta-V, estimation covariance, and system health. This controlled rollout de-risks certification while delivering operational savings early in the program lifecycle.

This workflow automates the high-frequency cycle of fusing star tracker, GPS, and ranging data to calculate precise orbital state vectors and execute fuel-optimal station-keeping maneuvers. It eliminates the latency and labor cost of ground-based orbit determination and maneuver planning, directly reducing operator workload and fuel consumption. The business case is clear: for a 100-satellite constellation, automating station-keeping can save thousands of hours of analyst time annually and extend asset life through more efficient propellant use, protecting hundreds of millions in asset value.

Implementation follows a phased rollout, beginning with shadow-mode validation where the agent's recommended maneuvers are compared against ground operations without execution. The second phase introduces execution for low-risk, low-delta-V maneuvers under tight telemetry watch. Full autonomy is granted only after statistical confidence is proven. Governance is enforced via digital twin simulation for each planned maneuver, anomaly detection agents monitoring telemetry for off-nominal thruster performance, and immutable audit logs linking every decision to the raw sensor data and logic state at the time of execution.

The operational upside—reducing ground-based tracking operations by 70% and optimizing fuel consumption—is locked behind integration complexity. New orchestration layers must ingest data from legacy star trackers, GPS receivers, and ranging systems via often-proprietary buses, then feed validated maneuver commands back into the certified attitude and orbit control system (AOCS). This creates a hybrid architecture where AI agents perform calculations, but the final command path runs through flight-proven, certified software modules to maintain safety assurance.

Implementation requires a phased rollout, starting with shadow mode where AI recommendations are compared against ground truth without actuation. Each integration point—data ingestion, calculation, and command handoff—needs a formal verification artifact for certification. The final architecture embeds the new orchestrator as a containerized payload, communicating via defined APIs with the core AOCS, ensuring the legacy system's flight certification envelope remains intact while enabling autonomous, fuel-optimal station-keeping.